Rethinking cancer targeting strategies in the era of smart cell therapeutics

In the past several decades, the development of cancer therapeutics has largely focused on precision targeting of single cancer-associated molecules. Despite great advances, such targeted therapies still show incomplete precision and the eventual development of resistance due to target heterogeneity or mutation. However, the recent development of cell-based therapies such as chimeric antigen receptor (CAR) T cells presents a revolutionary opportunity to reframe strategies for targeting cancers. Immune cells equipped with synthetic circuits are essentially living computers that can be programmed to recognize tumours based on multiple signals, including both tumour cell-intrinsic and microenvironmental. Moreover, cells can be programmed to launch broad but highly localized therapeutic responses that can limit the potential for escape while still maintaining high precision. Although these emerging smart cell engineering capabilities have yet to be fully implemented in the clinic, we argue here that they will become much more powerful when combined with machine learning analysis of genomic data, which can guide the design of therapeutic recognition programs that are the most discriminatory and actionable. The merging of cancer analytics and synthetic biology could lead to nuanced paradigms of tumour recognition, more akin to facial recognition, that have the ability to more effectively address the complex challenges of treating cancer. This Perspective outlines the preclinical emergence of smart cell therapeutics, which when paired with machine learning analysis of genomic data could be implemented in the clinic to both enhance tumour recognition and prevent tumour escape.

Automated summary

In the past, cancer therapeutics focused on precision targeting of single cancer-associated molecules, which still had limitations. However, the recent development of cell-based therapies, equipped with synthetic circuits, presents a revolutionary opportunity to target cancers in a more precise and effective way. Moreover, combining these smart cell engineering capabilities with machine learning analysis of genomic data could enhance tumor recognition and prevent tumor escape.